代写Problem set 11 Final Exam代做留学生SQL语言程序

Final Exam

Please answer all questions, displaying your work in aneat and orderly manner. You may download the exam PDF document.  Partial credit will be given to correct steps shown. This exam is open book, open notes and calculators maybe used. Communication devices are not allowed. Appropriate statistical

evidence is required in your responses.

You may use the space provided for your answers or upload scanned images (see the last item) of your responses.  For instance, you may download and print the exam pdf document, write (neatly) your

answers on it, scan and upload it.

Note that the data used in this exam is not real.

1.    A study looked at the impact of whether or not a first year student attended orientation and if they lived on campus or not on their overall satisfaction with student services (measured on a  ten point scale at the end of the first year).  A two by two factorial analysis of variance was

performed via multiple regression.  The results indicated a significant interaction.  Using the means table below, plot the interaction and interpret the results.

2.    Given the data and contrast codes below, write the estimated regression model Y(̂)i =

b0 +b1C1+b2C2

Descriptive statistics:

Contrasts:

3.    A highschool introduced an experiential learning component to their science courses in 11th grade.  In the past, all students participated in an in-school, applied project over the course of the school year.  This year, instead, they randomly assigned students to different programs/projects in and outside the school.  Group 1 was assigned to the traditional, existing in-school applied project.  Group 2 was assigned to participate in an engineering program at a  local firm, Group 3 was assigned to a medical research center and group 4 was assigned to a children’s science museum.  At the end of the school year, all students took an end of year assessment, aligned to the curriculum,  to determine their level of scientific skills and knowledge.  The scale scores range from 0 to 20.  To analyze the data and compare performance by group, the following codes (see below) were used.  Using the information below and the SPSS output, summarize the results of the analysis. Be concise but include all relevant statistical evidence.

Report

Y

Group	Mean	N	Std. Deviation
1.00	14.0833	12	2.31432
2.00	16.9167	12	2.27470
3.00	13.0000	12	1.90693
4.00	16.3333	12	2.18812
Total	15.0833	48	2.65645

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.609a	.371	.328	2.17684

a. Predictors: (Constant), D3, D2, D1

ANOVAa

Model	Sum of Squares	df	Mean Square	F	Sig.
1 Regression Residual Total	123.167	3	41.056	8.664	.000b
	208.500	44	4.739
	331.667	47

a. Dependent Variable: Y

b. Predictors: (Constant), D3, D2, D1

Coefficientsa

	Unstandardized		Standardized
Model	Coefficients		Coefficients	t	Sig.
	B	Std. Error	Beta
1 (Constant) D1 D2 D3	14.083	.628		22.411	.000
	2.833	.889	.467	3.188	.003
	-1.083	.889	-.178	-1.219	.229
	2.250	.889	.371	2.532	.015

a. Dependent Variable: Y

4.    An organization provides training courses in the use of their class scheduling software for

schools.  This particular course is delivered to administrators (participants) who had never previously used software to schedule classes. An end of course project is given to all participants and is graded out of 100 points.   The organization would like to test whether mode of delivery    (online (coded -1) or in person (coded 1)) results in different performance on the course ending   project.  To control for differences in computer knowledge, the organizers administer a computer software literacy test (50-point scale) prior to the training.  Participants are randomly assigned to the two modes of delivery (18 to each mode).  They conduct an analysis of covariance to determine if the different modes result in different scores on the end of course assessment.  Use the output on the following pages to summarize the findings.  Be concise while including the relevant statistical evidence.

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1 2	.943a .949b	.890 .901	.883 .892	1.57063 1.51222

ANOVAa

Model	Sum of Squares	df	Mean Square	F	Sig.
1 Regression Residual Total	656.593	2	328.296	133.081	.000b
	81.407	33	2.467
	738.000	35
2 Regression Residual Total	664.822	3	221.607	96.907	.000c
	73.178	32	2.287
	738.000	35